Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Detecting time series anomalies at Uber scale with recurrent neural networks

Andrea Pasqua (Uber), Anny Chen (Uber)
4:20pm–5:00pm Wednesday, March 7, 2018
Data science and machine learning
 Location: LL21 B
Secondary topics:  Graphs and Time-series
Average rating: ****.
(4.60, 5 ratings)
Download slides (PDF)

Who is this presentation for?

  • Data scientists, product managers, and executives

What you'll learn

  • Learn how Uber applies recurrent neural networks to time series analysis

Description

Time series forecasting and anomaly detection is of utmost importance at Uber. However, the scale of the problem, the need for speed, and the importance of accuracy make anomaly detection a challenging data science problem. Andrea Pasqua and Anny Chen explain how the use of recurrent neural networks is allowing Uber to meet this challenge.

Topics include:

  • Why time series are of great importance at Uber
  • The business implication of accurate and reliable forecasting
  • The need for a platform solution for anomaly detection
  • The methodology used
  • The results
  • Future developments
Photo of Andrea Pasqua

Andrea Pasqua

Uber

Andrea Pasqua is a data science manager at Uber, where he leads the time series forecasting and anomaly detection teams. Previously, Andrea was director of data science at Radius Intelligence, a company spearheading the use of machine learning in the marketing space; a financial analyst at MSCI, a leading company in the field of risk analysis; and a postdoctoral fellow in biophysics at UC Berkeley. He holds a PhD in physics from UC Berkeley.

Photo of Anny Chen

Anny Chen

Uber

Anny (Yunzhu) Chen is a senior data scientist at Uber working on time series anomaly detection and forecasting. Anny is passionate about applying statistical and machine learning models to real business problems. Previously, she was a data scientist at Adobe, where she worked on digital attribution modeling for customer conversion data. She holds an MS in statistics from Stanford University and a BS in probability and statistics from Peking University.